44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
from typing import Dict, List, Any
|
|
from tangoflux import TangoFluxInference
|
|
import torchaudio
|
|
#from huggingface_inference_toolkit.logging import logger
|
|
|
|
class EndpointHandler():
|
|
def __init__(self, path=""):
|
|
# Preload all the elements you are going to need at inference.
|
|
# pseudo:
|
|
# self.model= load_model(path)
|
|
self.model = TangoFluxInference(name='declare-lab/TangoFlux',device='cuda')
|
|
|
|
|
|
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""
|
|
data args:
|
|
inputs (:obj: `str` | `PIL.Image` | `np.array`)
|
|
kwargs
|
|
Return:
|
|
A :obj:`list` | `dict`: will be serialized and returned
|
|
"""
|
|
|
|
logger.info(f"Received incoming request with {data=}")
|
|
|
|
if "inputs" in data and isinstance(data["inputs"], str):
|
|
prompt = data.pop("inputs")
|
|
elif "prompt" in data and isinstance(data["prompt"], str):
|
|
prompt = data.pop("prompt")
|
|
else:
|
|
raise ValueError(
|
|
"Provided input body must contain either the key `inputs` or `prompt` with the"
|
|
" prompt to use for the audio generation, and it needs to be a non-empty string."
|
|
)
|
|
|
|
parameters = data.pop("parameters", {})
|
|
|
|
num_inference_steps = parameters.get("num_inference_steps", 50)
|
|
duration = parameters.get("duration", 10)
|
|
guidance_scale = parameters.get("guidance_scale", 3.5)
|
|
|
|
|
|
return self.model.generate(prompt,steps=num_inference_steps,
|
|
duration=duration,
|
|
guidance_scale=guidance_scale) |